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On spatial contagion and multivariate GARCH models

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  • Piotr Jaworski
  • Marcin Pitera

Abstract

We propose a method for defining and measuring spatial contagion between two financial markets via conditional copulas. Some theoretical results on monotonicity and asymptotic properties of Gaussian copulas with respect to conditioning are presented. Next, we combine the spatial contagion approach with time series models. We investigate which model from a large family of multivariate GARCH is the best tool for modelling spatial contagion. In an empirical study, we show that among models designed for general fit, a two‐step model fitting procedure reduces the ability to describe the contagion effect. This is a feature of copula‐GARCH models. Copyright © 2013 John Wiley & Sons, Ltd.

Suggested Citation

  • Piotr Jaworski & Marcin Pitera, 2014. "On spatial contagion and multivariate GARCH models," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 30(3), pages 303-327, May.
    • Handle: RePEc:wly:apsmbi:v:30:y:2014:i:3:p:303-327
    DOI: 10.1002/asmb.1977
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    Cited by:

    1. Karatetskaya Efrosiniya & Lakshina Valeriya, 2018. "Volatility Spillovers With Spatial Effects On The Oil And Gas Market," HSE Working papers WP BRP 72/FE/2018, National Research University Higher School of Economics.
    2. Piotr Jaworski & Marcin Pitera, 2015. "The 20-60-20 Rule," Papers 1501.02513, arXiv.org, revised Aug 2015.
    3. Valeria V. Lakshina, 2019. "Do Portfolio Investors Need To Consider The Asymmetry Of Returns On The Russian Stock Market?," HSE Working papers WP BRP 75/FE/2019, National Research University Higher School of Economics.
    4. Lakshina, Valeriya, 2020. "Do portfolio investors need to consider the asymmetry of returns on the Russian stock market?," The Journal of Economic Asymmetries, Elsevier, vol. 21(C).

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